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Re: st: How to implement Discrete Principal Component Analysis by using POLYCHORICPCA
From
汪哲仁 <[email protected]>
To
[email protected]
Subject
Re: st: How to implement Discrete Principal Component Analysis by using POLYCHORICPCA
Date
Fri, 6 Jan 2012 01:35:28 +0400
Dear Cam,
Thanks for your reply. Usually, pca procedure give us a
result like this.
=================================================================================
pca foreign rep78 mpg
Principal components/correlation Number of obs = 69
Number of comp. = 3
Trace = 3
Rotation: (unrotated = principal) Rho = 1.0000
--------------------------------------------------------------------------
Component | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Comp1 | 1.9703 1.34377 0.6568 0.6568
Comp2 | .626528 .223359 0.2088 0.8656
Comp3 | .403169 . 0.1344 1.0000
--------------------------------------------------------------------------
Principal components (eigenvectors)
----------------------------------------------------------
Variable | Comp1 Comp2 Comp3 | Unexplained
-------------+------------------------------+-------------
foreign | 0.6084 -0.2732 -0.7451 | 0
rep78 | 0.5910 -0.4706 0.6551 | 0
mpg | 0.5296 0.8390 0.1249 | 0
----------------------------------------------------------
===================================================================================
but polychoricpca only give us results without eigenvectores, like below.
polychoricpca foreign mpg rep78
k | Eigenvalues | Proportion explained | Cum. explained
----+---------------+------------------------+------------------
1 | 2.206757 | 0.735586 | 0.735586
2 | 0.615445 | 0.205148 | 0.940734
3 | 0.177798 | 0.059266 | 1.000000
My question is that, after running polychoricpca procedure, how can I
know the eigenvectors to construct an index.
Thanks for your attention.
with kind regards,
Charles Wang
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